1. Field of the Invention
The present invention relates to a category weight setting apparatus and a category weight setting method for setting a weight of each category obtained by classification of images, to an image weight setting apparatus and an image weight setting method for setting a weight of each of the images, and to programs that cause a computer to execute the category weight setting method and the image weight setting method.
The present invention also relates to a category abnormality setting apparatus and a category abnormality setting method for setting an abnormality of each category obtained by classification of images, and to a program that causes a computer to execute the category abnormality setting method.
2. Description of the Related Art
Following the spread of digital cameras and scanners, anyone can obtain digital images with ease. Images are also input to personal computers and classified. In this case, images are often classified into events related to users. For example, in the case of a trip to Europe, images are firstly classified into categories of countries such as France, the UK, and Spain, and images classified into the category of France are further classified into detailed categories of places visited, such as Paris and Nice. Images classified into the category of Paris are then classified into more detailed categories such as the Eiffel Tower, the Arch of Triumph, and the Notre Dame Cathedral. Images of Notre Dame Cathedral may further be classified into much more detailed categories such as “In Front Of The Cathedral”, “Away from The Cathedral”, and “Inside The Cathedral”.
If images are hierarchically classified into a plurality of categories, the images can be referred to later according to events and can be organized with ease.
On the other hand, users need to carry out hierarchical classification of images while viewing the images and refreshing their memories, which is a troublesome operation for the users.
For this reason, various methods have been proposed for automatically classifying images. For example, a method has been proposed wherein images are arranged along a time axis based on information representing time and date of photography added thereto and the images are classified into predetermined k categories by comparing a threshold value with difference in the time of photography between two of the images neighboring each other on the time axis (k-average clustering method, see Japanese Unexamined Patent Publication No. 2000-112997). Another method has also been proposed for classifying images into categories of events (see Japanese Unexamined Patent Publication No. 2003-141130). In this method, photography frequency is found by adding the number of images having been stored in each predetermined period, based on information on time and date of photography related to the images. The magnitude of the number of images having been photographed in a predetermined period, which is found according to the frequency, is then judged based on a predetermined threshold value. Based on the magnitude, images are classified into categories of events. Furthermore, another method has been proposed for classifying images into categories according to a schedule of a user by referring to schedule information representing the schedule of the user (see U.S. Patent Application Publication No. 20030184653). Still another method of image classification has also been proposed in U.S. Patent Application Publication No. 20050128305. In this method, the distance is calculated between a place of reference and a place of photography of an image by use of position information of the place of photography, and images are classified based on time of photography by changing a threshold value therefor according to the distance.
In addition, a method of calculating weights of images classified into categories has also been proposed in Japanese Patent Application No. 2004-360870. In this method, a weight of each of images classified into each category is calculated based on characteristics of the category such as the number of images therein, the number of images in an upper-level category, the number of related categories, the number of lower hierarchical levels, and the number of hierarchical levels from the uppermost level to the level thereof.
However, in the case where images have automatically been classified as has been described above, users cannot understand which category includes important images unless the users confirm the content of images in the categories.
In addition, in the case where images have been classified in the above manner, the manner of photography may become unnatural due to a relationship between the number of images in each category and the time of photography thereof, for example. The manner of photography becomes unnatural in the case where a large number of images have been photographed in a very short time or in the case where a very small number of images have been photographed in a very long time, for example. These cases occur when images photographed at the same time by a plurality of cameras are stored in the same folder or when some images are deleted after photography. In photography with a digital camera, the same scene is often photographed a plurality of times in case of failure of photography. Therefore, it is natural for the number of groups of images similar to each other to become larger as the number of images increases. However, image classification may be failed as in the case where the number of groups of images similar to each other becomes substantially smaller than the number of images.
If weights are calculated for images classified into categories based on characteristics of the categories such as the number of hierarchical levels as has been described in Japanese Patent Application No. 2004-360870 despite the fact that the images in the categories have not been classified normally due to an unnatural manner of photography or failure of classification, the weights cannot represent true weights.